Machines can be used to review an electronic version of an image to recognize information within an electronic image.
An object class is a collection of objects which share characteristic parts or features that are visually similar, and which occur in similar spatial configurations. Solutions to the problem of recognizing members of object classes have taken various forms. Use of various models have been suggested.
These techniques often require a training process that attempts to carry out:                segmentation, that is which objects are to be recognized and where do they appear in the training images,        selection, that is, selection of which object parts are distinctive and stable, and        estimation of model parameters, that is, what parameters of the global geometry or shape and the appearance of the individual parts best describe the training data.        
Previous model based techniques may require a supervised stage of learning. For example, targeted objects must be identified in training images either by selection of points or regions on their surfaces, or by segmenting the objects from the background. This may produce significant disadvantages, including that any prejudices of the human observer, such as which features appear most distinctive to the human observer, may also be trained during the training process.